A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2012-07-30 eCollection Date: 2012-01-01 DOI:10.4137/BECB.S9335
Farid Mobasser, Keyvan Hashtrudi-Zaad
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引用次数: 23

Abstract

In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.

Abstract Image

Abstract Image

Abstract Image

一种基于人工神经网络的手部力估计比较方法。
在许多包括人类直接参与的应用中,例如假肢手臂的控制、运动训练和肌肉生理学的研究,都需要手的力量来进行控制、建模和监测。与使用通常非常昂贵且需要笨重框架的力传感器相比,使用廉价且易于携带的主动肌电图(EMG)电极和位置传感器在这些应用中是有利的。在非基于模型的估计方法中,多层感知器人工神经网络(Multilayer Perceptron Artificial Neural Networks, MLPANN)被广泛用于从人类或动物的不同解剖特征中估计肌肉力或关节扭矩。本文研究了径向基函数(RBF)神经网络和MLPANN用于力估计的使用,并在实验中比较了两种方法在相同人体解剖结构(即手的力估计)下在操作条件集合下的性能。在这个统一的研究中,上臂肌肉参与肘关节运动的肌电图信号读数和肘关节的角位置和速度被用作人工神经网络的输入。此外,还研究了肘关节角加速度信号作为人工神经网络输入的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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